Motion-based music recommendation for mobile devices

ABSTRACT

A method comprising acquiring a plurality of measurements from at least one sensor in a mobile device, determining an activity classification of a user of the mobile device based on the plurality of measurements, acquiring an audio file for the mobile device, wherein the audio file is selected based on the activity classification, and playing the audio file by the mobile device.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 14/040,436 filed Sep. 27, 2013 by Chia-Chin Chong, et al. andentitled “Motion-Based Music Recommendation for Mobile Devices”, whichclaims priority to U.S. Provisional Patent Application No. 61/800,380filed Mar. 15, 2013 by Chia-Chin Chong, et al. and entitled“Motion-Based Music Recommendation System, Method and Service for MobileDevices”, both of which are incorporated herein by reference in theirentireties.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

REFERENCE TO A MICROFICHE APPENDIX

Not applicable.

BACKGROUND

Music recommendation systems and services such as Pandora, Ringo andSpotify are becoming an increasingly popular way for users to find andlisten to music that may be of interest to them. Most of these musicrecommendation systems identify music for the user to listen to based onthe user's personal preferences as indicated by the user through manualselection or some other type of affirmative user action indicating theuser's preferences (e.g., “like”).

Pandora is free personalized internet radio. The service plays musicalselections of a certain genre based on the user's artist selection. Theuser then provides positive or negative feedback for songs chosen by theservice, which are taken into account when Pandora selects or recommendsfuture songs to the user.

Pandora recommends songs based on a certain genre and artist that theuser has selected in advanced. Furthermore, the user needs to providepositive or negative feedback for songs chosen by the service, which aretaken into account when Pandora selects future songs to further improvethe music recommendation system.

Ringo is a music recommendation system accessible to users only viaemail. Users rate musical artists and then are able to receiverecommendations for further listening.

Spotify is a new way to enjoy music socially. Spotify does not recommendsongs based on individual preferences, but instead allows registeredusers to integrate their account with existing Facebook and Twitteraccounts. Once a user integrates their Spotify account with other socialmedia profiles, they are able to access their friends' favorite musicand playlists. Because music is social, Spotify allows you to sharesongs and playlists with friends, and even work together oncollaborative playlists.

However, existing music recommendation systems and services such as theabove are relatively inflexible in that they generally do not take intoaccount the changing music preferences of users of mobile devices (e.g.,smartphones) from moment to moment as they engage in differentactivities or enter different environments. Mobile device userstypically use their devices on the go while engaged in various differentactivities and located in environments in which their music listeningpreferences may change from moment to moment. Requiring users tomanually set or change their personal music listening preferences ontheir mobile device can be inconvenient as they are constantly changingbetween different activities or environments, especially considering thelimited user interface currently provided by mobile devices.

In view of the above, there is a need for a music recommendation systemand service for users of mobile devices such as smartphones that bettertakes the changing music preferences of the user into account.

SUMMARY

In at least one embodiment, the disclosure includes a method comprisingacquiring a plurality of measurements from at least one sensor in amobile device, determining an activity classification of a user of themobile device based on the plurality of measurements, acquiring an audiofile for the mobile device, wherein the audio file is selected based onthe activity classification, and playing the audio file by the mobiledevice.

In at least one embodiment, the disclosure includes a computer programproduct comprising computer executable instructions stored on anon-transitory computer readable medium such that when executed by aprocessor cause a mobile device to acquire a plurality of measurementsfrom at least one sensor in a mobile device, determine an activityclassification of a user of the mobile device based on the plurality ofmeasurements, acquire an audio file for the mobile device, wherein theaudio file is selected based on the activity classification, and playthe audio file by the mobile device.

In at least one embodiment, the disclosure includes a mobile devicecomprising at least one sensor configured to generate a plurality ofmeasurements, a processor coupled to the at least one sensor andconfigured to acquire the plurality of measurements, determine anactivity classification of a user of the mobile device based on theplurality of measurements, and acquire an audio file, wherein the audiofile is selected based on the activity classification.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a logical view illustrating the structure and function of anembodiment of an automatic personalized music recommendation system.

FIG. 2 shows an illustrative embodiment of a sensor fusion platform.

FIG. 3 shows an illustrative embodiment of an activity identifier.

FIG. 4 shows an illustrative embodiment of a music classifier.

FIG. 5 shows an illustrative embodiment of a conceptual contextawareness platform.

FIG. 6 shows an illustrative embodiment of music recommender.

FIGS. 7, 8 and 9 are flowcharts illustrating embodiments of methods forgenerating music recommendations to the user based on sensor input anduser intent/inference/prediction of the context awareness platform.

FIG. 10 illustrates an embodiment of a music recommendation system.

FIG. 11 is a schematic diagram of an embodiment of a mobile device.

DETAILED DESCRIPTION

It should be understood at the outset that, although an illustrativeimplementation of one or more embodiments are provided below, thedisclosed systems and/or methods may be implemented using any number oftechniques, whether currently known or in existence. The disclosureshould in no way be limited to the illustrative implementations,drawings, and techniques illustrated below, including the exemplarydesigns and implementations illustrated and described herein, but may bemodified within the scope of the appended claims along with their fullscope of equivalents. While certain aspects of conventional technologieshave been discussed to facilitate the present disclosure, thesetechnical aspects are in no way disclaimed, and it is contemplated thatthe present disclosure may encompass one or more of the conventionaltechnical aspects discussed herein.

In view of the limitations of existing music recommendation systems andservices as described above, a need is recognized for a personalizedmusic recommendation system, method and service for mobile wirelesscommunication devices (or “mobile devices” for short), such assmartphones or cell phones, that takes the mobile device user's inferredmood or emotions into account.

In at least some embodiments, the disclosed music recommendation systemexploits the sensors that exist on mobile devices (and other wearabledevices that can be connected with mobile devices) and combines themwith mood-based music classification to make personalized musicrecommendations based on the user's current physical motion and inferredactivities and/or mood. The system maps physical motion with the user'smood through the use of a context awareness platform learning system.

The type of music or song to be recommended to the user may be based ona mood categories, which can be obtained from a context awarenessplatform in a mobile device. The context awareness platform may infer amood of a user from physical activity as measured by one or more sensorsin the mobile device. In this manner, this music recommendation methodand system quickly and automatically adapts the recommendations based onchanges in the user's activities and environment. The disclosed musicrecommendation system does not require manual intervention or expressaction by the user and quickly adapts to changes in the user's inferredmood or preferences from moment to moment.

While many of the embodiments are discussed in the context of a mobiledevice such as a smartphone, it may be implemented on any portableelectronic device with physical and/or virtual sensors that is capableof playing music (e.g., Moving Picture Experts Group (MPEG)-1 (MPEG-1)or MPEG-2 Audio Layer III (MP3) player, tablet computer, wristcomputer). Also, although in this illustrative embodiment the musicrecommendation method is implemented in a mobile device, one of ordinaryskill in the art could readily implement the method as a cloud-basedservice for a mobile device. Further, although the illustrativeembodiments are described in the context of recommending music contentto the user, one of ordinary skill in the art could readily adapt themethod to recommending other types of content to the user, such asvideos, animated graphics, and web sites.

FIG. 1 is a logical view of the structure and function of an embodimentof an automatic personalized music recommendation system 100. The system100 includes mobile device sensors 101, sensor fusion platform 102,activity identifier 103, audio analysis/music classification platform106, mood classifier 107, context awareness platform 104 and musicrecommender 105, which work together as indicated to automaticallyprovide personalized music recommendations to a user of a mobile device.

The system 100 also includes audio files 108. Each audio file in theaudio files 108 may be a recorded song or music. An audio file may besaved in any audio format, such as Advanced Audio Coding (AAC) or MP3.The audio files 108 may be stored in any of a number of locations. Forexample, some or all of the audio files may be stored locally on amobile device and some or all of the audio files may be stored in acloud-based storage application or system.

In addition to audio files 108, at least a portion of the system 100 maybe part of the mobile device. A portion of the system 100 may be part ofa cloud-based storage and processing system as explained further below.

Sensors 101 are physical sensors that are embedded in the mobile deviceand are used to obtain data on the physical motion of the user of themobile device. The mobile device sensors 101 may include accelerometers,magnetometers, gyroscopes, pressure sensors, a Global Positioning System(GPS) device, or any other type of sensor for measuring position and/ororientation of the mobile device. The type of information provided bythe listed sensor types are understood by a person of ordinary skill inthe art. For example, an accelerometer is commonly used in mobiledevices for user interface control. An accelerometer in such anapplication measures an orientation of the mobile device and may adjusta user interface or display accordingly. Accelerometers may also be usedin pedometer applications to measure a number of steps taken by a user.As another example, a magnetometer may use the fact that a direction ofthe Earth's magnetic field at or near the Earth's surface may be known.The various mobile device sensors 101 provides inputs into the sensorfusion platform 102.

FIG. 2 shows an illustrative embodiment of the sensor fusion platform102. The sensor fusion platform 102 receives and combines the raw datacollected from sensors 101 for input into activity identifier 103.Sensor fusion platform 102 may include sensor data analysis andpost-processing and sensor fusion classification. Sensor data analysismay be implemented using a Kalman filter (i.e., linear quadraticestimation) or other type of filtering as is well known in the art.Sensor fusion classification may be implemented using Bayesian models,hidden Markov models, neural networks, etc. The sensor fusion platform102 may be implemented as a module or computer program instructions in acomputing system, such as a mobile device.

FIG. 3 shows an illustrative embodiment of activity identifier 103.Activity identifier 103 is a table of possible physical activities thatwill be inferred by sensor fusion platform 102 based on sensing datacollected from sensors 101. These physical activities may includesitting, walking, running, driving, etc. as shown in FIG. 4. The sensorfusion platform 102 and the activity identifier 103 work together toaccept sensor data from sensors 101 in a mobile device and identify anactivity of a user of the mobile device based on the sensor data.

FIG. 4 shows an illustrative embodiment of mood classifier 107. Moodclassifier 107 is a table of possible moods (or emotional states) thatcan be used to classify each of the songs or audio files. These moodscould include happy, ecstatic, content, sad, depressed, nervous, angry,bored, tired, hyper, excited, grumpy, aggressive, etc. Mood classifier107 may tag each song or audio file with one or more moods or emotionalstates that may be based on learning and feedback behavior from contextaware platform 104 as explained more fully below.

FIG. 5 shows an illustrative embodiment of conceptual context awarenessplatform 104. Context awareness platform 104 may include the followingthree components: (1) user intent/inference/prediction, (2) usermodel/situational model and (3) machine learning. The user modelcomponent includes: (1) a user model specifying significant locations,time patterns, preferences and relations, (2) user model algorithms and(3) user behavior modeling. The situational model component includes:(1) a situational model specifying current locations, time relation,motion status, active relationships, current activity, (2) situationalmodel algorithms and (3) context modeling.

FIG. 6 shows an illustrative embodiment of music recommender 105. Musicrecommender 105 is a table of possible music types or genres that areprovided as input to context awareness platform 104. These music typescould include alternative, blues, classical, etc. Music recommender 105is a learning system that will improve its recommendation accuracy withtime.

FIGS. 7, 8 and 9 are flowcharts showing the process in which the musicrecommendation system shown in FIG. 1 generates music recommendationsbased on sensor input and user intent/inference/prediction of thecontext awareness platform.

FIG. 7 is a flowchart of an embodiment of a method 700 for classifyingmotion of a user into an activity classification FIG. 7 illustrates howraw data obtained from sensors 101 of a mobile device may be analyzedand processed by the sensor fusion platform 102 to generate the vectorof feature values, which will then be classified by sensor fusionplatform 102 into activity identifier 103. In block 710, data isobtained from sensors, such as sensors 101. The data may be position,rotation/orientation, or motion data regarding the mobile device inwhich the sensors 101 are embedded. Such data may reflect motion of auser of the mobile device. The data may be provided to a sensor fusionplatform 102. In block 720, feature values are determined from sensingdata. In block 730, data analysis and post-processing of the sensingdata may be performed. In this block, Kalman filtering and other signalprocessing may be performed on the data, e.g., to determine a pattern ofspeed and/or orientation and/or motion. Feature values of the underlyingdata may be determined. In block 740, the results of block 730 are usedto classify the activity of the user into one of the activityidentifiers, e.g., one of the activities listed in FIG. 3. Sensor fusionclassification may be performed in block 740, which may involve theconstruction of Bayesian models, Markov models, and/or neural networks.These are well-known statistical models/methods typically used toprocess huge amounts of data. A benefit would be the capability of thesemodels to handle large dimensional data. In block 750, a decision ismade whether activity classification is complete. A determination ofwhether it is complete may be based on whether sufficient data has beencollected to provide confidence in the activity classification or it maybe based on whether a sufficient amount of time has passed since sensordata collection began. If activity classification is complete, block 760is performed, in which an “activity identifier” is selected. Theactivity identifier may be one of the activities listed in FIG. 3. Theselected activity identifier may be provided as an input to the contextawareness platform 104. If activity classification is not complete, theflowchart returns to block 710 in which more sensing data is collected.

FIG. 8 is a flowchart of an embodiment of a method 800 for classifyingan audio file into one or more moods. The method 800 shows how audiofiles, e.g., audio files 108, are obtained & sorted in order to performboth the mood-independent audio analysis & mood-based musicclassification in audio analysis/music classification platform 106 (or“platform 106”), which will then classified by the platform 106 intomood classifier 107. The method 800 begins in block 810. In block 810,audio files may be obtained from available sources, such as from cloudstorage, local storage on a mobile device, etc. The platform 106 mayreceive one or more audio files as inputs. In block 820,mood-independent audio analysis is performed, which may include featureextraction, summarization, and pre-processing of the audio signalrepresented by an audio file. In block 830, mood-based musicclassification may be performed, e.g., using mood detection algorithmsor regressor training in order to map or classify an audio file into oneor more of the moods in the mood classifier 107. In decision block 840,a decision is made whether mood-based classification is complete. Ifclassification is complete, a mood classifier is selected in block 850for the audio files. After this process, an audio file will have atleast one associated mood. The mood may be one of those shown in FIG. 4.A database of audio file names and associated moods may be created basedon the method 800. The platform 106 may perform blocks 810-840.

Note that the method 800 for classifying an audio file into a mood canbe performed offline (e.g., using a server or multiple servers in anetwork) and the database of audio file names and associate moods may bestored using any storage medium local or remote to a mobile device.

FIG. 9 is a flowchart of a method 900 for making a music recommendation.In block 910, an activity identifier (e.g., activity identifier 103) anda mood classifier (e.g., mood classifier 107) may serve as inputs. Theseinputs may be derived as shown in FIGS. 7 and 8. A context awarenessplatform may acquire or receive the inputs. In block 920, amachine-learning algorithm is performed that learns how to correlateactivity to mood based on user feedback (if any). At the outset beforeany user feedback, the method 900 includes a set of initial conditionsthat correlates or maps user activity to mood. In block 930, a mappingof activity identifier to mood classifier is performed which is used togenerate a music recommendation. In summary, in at least one embodimentsensor data leads to a user activity classification which maps to a moodwhich maps to a music genre which provides a music recommendation. FIG.9 illustrates how an activity identifier 103 and mood classifier 107 maybe used as inputs to the context awareness platform 104 in order for themusic recommender 105 to generate music recommendations for a user.

FIG. 10 illustrates an embodiment of a music recommendation system,including application (e.g., music recommendation system), contextawareness platform, and operating system of the mobile device.

FIG. 11 is a block diagram of a mobile device 1100 that may be used toimplement the music recommendation method and system disclosed herein.Mobile device 1100 may comprise a processor 1120 (which may be referredto as a central processor unit or CPU) that is in communication withmemory devices including secondary storage 1121, read only memory (ROM)1122, and random access memory (RAM) 1123. The processor 1120 may beimplemented as one or more general purpose CPU chips, one or more cores(e.g., a multi-core processor), one or more application specificintegrated circuits (ASICs) and/or one or more digital signal processors(DSPs). The processor 1120 may be configured to implement any of theschemes described herein, and may be implemented using hardware,software, firmware, or combinations thereof.

The secondary storage 1121 may be comprised of one or more solid statedrives, disk drives, and/or other memory types and is used fornon-volatile storage of data and as an over-flow data storage device ifRAM 1123 is not large enough to hold all working data. Secondary storage1121 may be used to store programs that are loaded into RAM 1123 whensuch programs are selected for execution. The ROM 1122 may be used tostore instructions and perhaps data that are read during programexecution. ROM 1122 may be a non-volatile memory device that may have asmall memory capacity relative to the larger memory capacity ofsecondary storage 1121. The RAM 1123 may be used to store volatile dataand perhaps to store computer instructions. Access to both ROM 1122 andRAM 1123 may be faster than to secondary storage 1121.

The mobile device 1100 may communicate data (e.g., packets) wirelesslywith a network via a network access point (not shown). As such, themobile device 1100 may comprise a receiver (Rx) 1112, which may beconfigured for receiving data (e.g. wireless packets or frames) fromother components. The receiver 1112 may be coupled to the processor1120, which may be configured to process the data and determine to whichcomponents the data is to be sent. The mobile device 1100 may alsocomprise a transmitter (Tx) 1132 coupled to the processor 1120 andconfigured for transmitting data to other components, for example byusing protocols such as Institute of Electrical and ElectronicsEngineers (IEEE) 802.11, IEEE 802.16, 3rd Generation Partnership Project(3GPP), Global System for Mobile Communications (GSM), or similarwireless protocols. The receiver 1112 and the transmitter 1132 may becoupled to at least one antenna 1130, which may be configured to receiveand transmit wireless radio frequency (RF) signals. In some embodiments,Tx 1132 and Rx 1112 may be replaced by a transceiver comprising thefunctionality of both Tx 1132 and Rx 1112. If the context awarenessplatform 104 and logic for selecting an audio file based on activityidentifier is located in a cloud-based application, the Tx 1132 may beused to communicate the activity identifier to the cloud-basedapplication. The cloud-based application may return an audio fileselection. The audio file selection may be stored on the mobile devicein, e.g., secondary storage 1121 or the audio file selection may bestored in a cloud-based storage application. If audio files (e.g., someor all audio files 108) are stored remotely, the Rx 1112 may be used toreceive audio files.

The mobile device 1100 may also comprise a display device 1140 coupledto the processor 1120, that displays output thereof. The mobile device1100 and the display device 1140 may be configured to displayrepresentations of data, which may be visible to a user. The displaydevice 1140 may comprise a color super twisted nematic (CSTN) display, athin film transistor (TFT) display, a thin film diode (TFD) display, anorganic light-emitting diode (OLED) display, an active-matrix OLEDdisplay, or any other display screen. The display device 1140 maydisplay in color or monochrome and may be equipped with a touch sensorbased on resistive and/or capacitive technologies.

The mobile device 1100 may further comprise an input/output (I/O) device1141 coupled to the processor 1120, which may allow the user to inputcommands to the mobile device 1100. Although labeled as a single device,the I/O device 1141 may comprise multiple devices. In the case that thedisplay device 1140 comprises a touch sensor, the display device 1140may also be considered the I/O device 1141. In addition to and/or in thealternative, an I/O device 1141 may comprise a mouse, trackball,built-in keyboard, external keyboard, and/or any other device that auser may employ to interact with the mobile device 1100. The I/O device1141 may comprise one or more speakers or headset jacks for providingaudio signals. The processor 1120 may convert a digital audio file to ananalog audio signal for transmission via the I/O device 1141 to beenjoyed by a user.

The mobile device 1100 may further comprise one or more sensors 1160,such as the sensors 101 described previously. The sensors 1160 mayinclude accelerometers, magnetometers, gyroscopes, pressure sensors,and/or a GPS device as examples. The sensors 1160 provide sensor data tothe processor 1120.

It is understood that by programming and/or loading computer executableinstructions onto the mobile device 1100, at least one of the processor1120, memory 1121-1123, and/or Rx/Tx 1112/1132 are changed, transformingthe mobile device 1100 in part into a particular machine or apparatus,e.g., a sensor fusion platform 102, an activity identifier 103, acontext awareness platform 104, a music recommender 105, an audioanalysis and music classification platform 106, and/or a mood classifier107 as described herein. It is fundamental to the electrical engineeringand software engineering arts that functionality that can be implementedby loading executable software into a computer can be converted to ahardware implementation by well-known design rules. Decisions betweenimplementing a concept in software versus hardware typically hinge onconsiderations of stability of the design and numbers of units to beproduced rather than any issues involved in translating from thesoftware domain to the hardware domain. Generally, a design that isstill subject to frequent change may be preferred to be implemented insoftware, because re-spinning a hardware implementation is moreexpensive than re-spinning a software design. Generally, a design thatis stable that will be produced in large volume may be preferred to beimplemented in hardware, for example in an ASIC, because for largeproduction runs the hardware implementation may be less expensive thanthe software implementation. Often a design may be developed and testedin a software form and later transformed, by well-known design rules, toan equivalent hardware implementation in an application specificintegrated circuit that hardwires the instructions of the software. Inthe same manner as a machine controlled by a new ASIC is a particularmachine or apparatus, likewise a computer that has been programmedand/or loaded with executable instructions may be viewed as a particularmachine or apparatus.

What is claimed is:
 1. A method comprising: acquiring a plurality ofmeasurements of a mobile device from at least one sensor in the mobiledevice; determining an activity classification of a user of the mobiledevice based on the plurality of measurements, the activityclassification identifying an activity of a plurality of activities;mapping the activity to a mood from a plurality of moods, wherein anaudio file obtained by the mobile device is associated with the mood;and playing the audio file associated with the mood to the user throughan output of the mobile device, wherein the audio file is selected basedon the activity classification and the mood.
 2. The method of claim 1,wherein the mobile device stores a mapping of each of a plurality ofactivity classifications to a corresponding one of the plurality ofmoods.
 3. The method of claim 2, wherein the mapping is pre-determined.4. The method of claim 3, wherein the mobile device receives a feedbackfrom a user input signal about the audio file, wherein the mapping isadjusted based on the feedback, and wherein a new audio file is selectedbased on the adjusted mapping.
 5. The method of claim 1, whereinselecting the audio file comprises retrieving the audio file from adatabase, wherein the database stores the plurality of moods and aplurality of audio files for each of the plurality of moods.
 6. Themethod of claim 1, wherein the plurality of measurements comprises aplurality of position measurement values, and wherein determining theactivity classification comprises analyzing the plurality of positionmeasurement values to determine the activity classification.
 7. Themethod of claim 1, wherein the plurality of activities comprises one ormore of standing, sitting, lying down, walking, running, biking,dancing, riding, strolling, and skating.
 8. A computer program productcomprising computer executable instructions stored on a non-transitorycomputer readable medium such that when executed by a processor cause amobile device to: acquire a plurality of measurements of a mobile devicefrom at least one sensor in the mobile device; determine an activityclassification of a user of the mobile device based on the plurality ofmeasurements, the activity classification identifying an activity of aplurality of activities; map the activity to a mood from a plurality ofmoods, wherein an audio file obtained by the mobile device is associatedwith the mood; and play the audio file associated with the mood to theuser through an output of the mobile device, wherein the audio file isselected based on the activity classification and the mood.
 9. Thecomputer program product of claim 8, wherein the mobile device stores amapping of each of a plurality of activity classifications to acorresponding one of the plurality of moods.
 10. The computer programproduct of claim 9, wherein the mobile device receives a feedback from auser input signal about the audio file, wherein the computer programproduct further comprises instructions to: adjust the mapping based onthe feedback; and select a new audio file based on the adjusted mapping.11. The computer program product of claim 8, wherein the plurality ofmeasurements comprises a plurality of position measurement values,wherein determining the activity classification comprises analyzing theplurality of position measurement values to determine the activityclassification.
 12. A mobile device comprising: at least one sensorconfigured to generate a plurality of measurements; a processor coupledto the at least one sensor and configured to: acquire the plurality ofmeasurements of the mobile device from the at least one sensor;determine an activity classification of a user of the mobile devicebased on the plurality of measurements, the activity classificationidentifying an activity of a plurality of activities; map the activityto a mood from a plurality of moods, wherein an audio file obtained bythe mobile device is associated with the mood; and play the audio fileassociated with the mood to the user through an output of the mobiledevice, wherein the audio file is selected based on the activityclassification and the mood.
 13. The mobile device of claim 12, whereinthe processor coupled to an audio output device, and wherein theprocessor is further configured to: convert the audio file into a signalfor use by the audio output device and send the signal to the audiooutput device.
 14. The mobile device of claim 12, further comprising amemory configured to store a mapping of each of a plurality of activityclassifications to a corresponding one of the plurality of moods. 15.The mobile device of claim 14, further comprising: a touch screenconfigured to receive a feedback from a user input signal about theaudio file, wherein the processor is further configured to: adjust themapping based on the feedback; and select a new audio file based on theadjusted mapping.
 16. The mobile device of claim 15, wherein the atleast one sensor comprises an accelerometer, wherein the plurality ofmeasurements comprises a plurality of accelerometer measurement values,and wherein determining the activity classification comprises analyzingthe plurality of accelerometer measurement values to determine theactivity classification.
 17. The mobile device of claim 12, wherein theplurality of activities comprises one or more of standing, sitting,lying down, walking, running, biking, dancing, riding, strolling, andskating.